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  <div class="section" id="mindspore-nn-grucell">
<h1>mindspore.nn.GRUCell<a class="headerlink" href="#mindspore-nn-grucell" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="mindspore.nn.GRUCell">
<em class="property">class </em><code class="sig-prename descclassname">mindspore.nn.</code><code class="sig-name descname">GRUCell</code><span class="sig-paren">(</span><em class="sig-param">input_size: int</em>, <em class="sig-param">hidden_size: int</em>, <em class="sig-param">has_bias: bool = True</em><span class="sig-paren">)</span><a class="headerlink" href="#mindspore.nn.GRUCell" title="Permalink to this definition">¶</a></dt>
<dd><p>GRU（Gate Recurrent Unit）称为门控循环单元。</p>
<div class="math notranslate nohighlight">
\[\begin{split}\begin{array}{ll}
r = \sigma(W_{ir} x + b_{ir} + W_{hr} h + b_{hr}) \\
z = \sigma(W_{iz} x + b_{iz} + W_{hz} h + b_{hz}) \\
n = \tanh(W_{in} x + b_{in} + r * (W_{hn} h + b_{hn})) \\
h' = (1 - z) * n + z * h
\end{array}\end{split}\]</div>
<p>这里 <span class="math notranslate nohighlight">\(\sigma\)</span> 是sigmoid激活函数， <span class="math notranslate nohighlight">\(*\)</span> 是乘积。 <span class="math notranslate nohighlight">\(W,b\)</span> 是公式中输出和输入之间的可学习权重。例如， <span class="math notranslate nohighlight">\(W_{ir}, b_{ir}\)</span> 是用于将输入 <span class="math notranslate nohighlight">\(x\)</span> 转换为 <span class="math notranslate nohighlight">\(r\)</span> 的权重和偏置。详见论文 <a class="reference external" href="https://aclanthology.org/D14-1179.pdf">Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation</a> 。</p>
<p><strong>参数：</strong></p>
<ul class="simple">
<li><p><strong>input_size</strong> (int) - 输入的大小。</p></li>
<li><p><strong>hidden_size</strong> (int) - 隐藏状态大小。</p></li>
<li><p><strong>has_bias</strong> (bool) - cell是否有偏置项 <cite>b_ih</cite> 和 <cite>b_hh</cite> 。默认值：True。</p></li>
</ul>
<p><strong>输入：</strong></p>
<ul class="simple">
<li><p><strong>x</strong> (Tensor) - shape为(batch_size, <cite>input_size</cite> )的Tensor。</p></li>
<li><p><strong>hx</strong> (Tensor) - 数据类型为mindspore.float32、shape为(batch_size, <cite>hidden_size</cite> )的Tensor。 <cite>hx</cite> 的数据类型必须与 <cite>x</cite> 相同。</p></li>
</ul>
<p><strong>输出：</strong></p>
<ul class="simple">
<li><p><strong>h’</strong> (Tensor) - shape为(batch_size, <cite>hidden_size</cite>)的Tensor。</p></li>
</ul>
<p><strong>异常：</strong></p>
<ul class="simple">
<li><p><strong>TypeError</strong> - <cite>input_size</cite> 、 <cite>hidden_size</cite> 不是int。</p></li>
<li><p><strong>TypeError</strong> - <cite>has_bias</cite> 不是bool值。</p></li>
</ul>
<p><strong>支持平台：</strong></p>
<p><code class="docutils literal notranslate"><span class="pre">Ascend</span></code> <code class="docutils literal notranslate"><span class="pre">GPU</span></code> <code class="docutils literal notranslate"><span class="pre">CPU</span></code></p>
<p><strong>样例：</strong></p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">GRUCell</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">16</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">hx</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">16</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">output</span> <span class="o">=</span> <span class="p">[]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="gp">... </span>    <span class="n">hx</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">hx</span><span class="p">)</span>
<span class="gp">... </span>    <span class="n">output</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">hx</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="go">(3, 16)</span>
</pre></div>
</div>
</dd></dl>

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